{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1、数据读取\n",
    "\n",
    "# 2、数据清洗\n",
    "\n",
    "# 3、数据重构\n",
    "\n",
    "# 4、建模预测提交"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要做的就是通过对历史33个月的数据来进行预测得到第三十四个月的销售情况item_cnt_month,用rmse来作为评估标准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling  as ppf\n",
    "\n",
    "from sklearn.externals import joblib##模型的保存\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')  # 关闭警告模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "##数据的读取\n",
    "train = pd.read_csv(\"train/sales_train_v2.csv\")#训练集\n",
    "test = pd.read_csv('test/test.csv')#测试集\n",
    "shops = pd.read_csv('shops.csv')#商品集\n",
    "items = pd.read_csv('items.csv')#\n",
    "item_categories = pd.read_csv('item_categories.csv')\n",
    "\n",
    "# print('--------------------------------------------------------------------')\n",
    "# print (train.head())\n",
    "# print('--------------------------------------------------------------------')\n",
    "# print (items.head())\n",
    "# print('-------------------------------------------------------------------')\n",
    "# print (shops.head())\n",
    "# print('-------------------------------------------------------------------')\n",
    "# print (item_categories.head())\n",
    "# print('-------------------------------------------------------------------')\n",
    "# print (test.head())\n",
    "\n",
    "# # checking the shapes of these datasets\n",
    "# print(\"Shape of train:\", train.shape)\n",
    "# print(\"Shape of test:\", test.shape)\n",
    "# print(\"Shape of shops:\", shops.shape)\n",
    "# print(\"Shape of items:\", items.shape)\n",
    "# print(\"Shape of item_categories:\", item_categories.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>22154</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2554</td>\n",
       "      <td>1709.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2555</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n",
       "0  02.01.2013               0       59    22154      999.00           1.0\n",
       "1  03.01.2013               0       25     2552      899.00           1.0\n",
       "2  05.01.2013               0       25     2552      899.00          -1.0\n",
       "3  06.01.2013               0       25     2554     1709.05           1.0\n",
       "4  15.01.2013               0       25     2555     1099.00           1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看一下异常值 可视化,为什么出不来图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 464 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "plt.figure(figsize=(10,4))#图的大小\n",
    "plt.xlim(-100, 3000)#范围\n",
    "sns.boxplot(x=train.item_cnt_day)#自变量\n",
    "# plt.show()\n",
    "plt.figure(figsize=(10,4))#图的大小\n",
    "plt.xlim(train.item_price.min(), train.item_price.max()*1.1)#最大值乘以1.1\n",
    "sns.boxplot(x=train.item_price)#自变量\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "##先对每日销售的产品数量进行修改\n",
    "train = train[train.item_cnt_day<1001]\n",
    "train = train[train.item_cnt_day>0]\n",
    "##再对产品的销售价格进行修改\n",
    "train = train[train.item_price<250000]\n",
    "train = train[train.item_price>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>22154</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2554</td>\n",
       "      <td>1709.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2555</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2564</td>\n",
       "      <td>349.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n",
       "0  02.01.2013               0       59    22154      999.00           1.0\n",
       "1  03.01.2013               0       25     2552      899.00           1.0\n",
       "3  06.01.2013               0       25     2554     1709.05           1.0\n",
       "4  15.01.2013               0       25     2555     1099.00           1.0\n",
       "5  10.01.2013               0       25     2564      349.00           1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()##这里只对两个变量进行了一个可视化，，，，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据重做--聚焦于item_cnt_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对日期这列进行变换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 6.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train['date']= pd.to_datetime(train['date'], format='%d.%m.%Y')##官网进行查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2013-01-02\n",
       "1   2013-01-03\n",
       "3   2013-01-06\n",
       "4   2013-01-15\n",
       "5   2013-01-10\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"date\"].head()##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(423681, 36)\n",
      "date  item_id  shop_id  2013-01  2013-02  2013-03  2013-04  2013-05  2013-06  \\\n",
      "0           0       54        0        0        0        0        0        0   \n",
      "1           1       55        0        0        0        0        0        0   \n",
      "2           2       54        0        0        0        0        0        0   \n",
      "3           3       54        0        0        0        0        0        0   \n",
      "4           4       54        0        0        0        0        0        0   \n",
      "\n",
      "date  2013-07  2013-08   ...     2015-01  2015-02  2015-03  2015-04  2015-05  \\\n",
      "0           0        0   ...           0        0        0        0        0   \n",
      "1           0        0   ...           0        0        0        0        0   \n",
      "2           0        0   ...           0        0        0        0        0   \n",
      "3           0        0   ...           0        0        0        0        0   \n",
      "4           0        0   ...           0        0        0        0        0   \n",
      "\n",
      "date  2015-06  2015-07  2015-08  2015-09  2015-10  \n",
      "0           0        0        0        0        0  \n",
      "1           0        0        0        0        0  \n",
      "2           0        0        0        0        0  \n",
      "3           0        0        0        0        0  \n",
      "4           0        0        0        0        0  \n",
      "\n",
      "[5 rows x 36 columns]\n",
      "Wall time: 24.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 将数据转换为月度销售数据\n",
    "\n",
    "# 制作仅包含月度销售数据的数据集\n",
    "data = train.groupby([train['date'].apply(lambda x: x.strftime('%Y-%m')),'item_id','shop_id']).sum().reset_index()\n",
    "##https://blog.csdn.net/Leonis_v/article/details/51832916--关于groupby的解释\n",
    "##https://www.runoob.com/python/att-time-strftime.html--关于strftime的解释\n",
    "# data.head()\n",
    "# 指定我们要添加到数据的重要属性\n",
    "data = data[['date','item_id','shop_id','item_cnt_day']]\n",
    "\n",
    "# 最后，我们可以从数据集中选择重要的特定属性\n",
    "data = data.pivot_table(index=['item_id','shop_id'], columns = 'date', values = 'item_cnt_day', fill_value = 0).reset_index()\n",
    "##https://zhuanlan.zhihu.com/p/31952948--关于pivot_table的解释\n",
    "\n",
    "print (data.shape)\n",
    "print (data.head())#目的是把每一个月的放到一起"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  shop_id  item_id\n",
       "0   0        5     5037\n",
       "1   1        5     5320\n",
       "2   2        5     5233\n",
       "3   3        5     5232\n",
       "4   4        5     5268"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()#训练集和测试集变成一样就好了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>2013-01</th>\n",
       "      <th>2013-02</th>\n",
       "      <th>2013-03</th>\n",
       "      <th>2013-04</th>\n",
       "      <th>2013-05</th>\n",
       "      <th>2013-06</th>\n",
       "      <th>2013-07</th>\n",
       "      <th>...</th>\n",
       "      <th>2015-01</th>\n",
       "      <th>2015-02</th>\n",
       "      <th>2015-03</th>\n",
       "      <th>2015-04</th>\n",
       "      <th>2015-05</th>\n",
       "      <th>2015-06</th>\n",
       "      <th>2015-07</th>\n",
       "      <th>2015-08</th>\n",
       "      <th>2015-09</th>\n",
       "      <th>2015-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  shop_id  item_id  2013-01  2013-02  2013-03  2013-04  2013-05  2013-06  \\\n",
       "0   0        5     5037      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1   1        5     5320      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2   2        5     5233      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3   3        5     5232      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "4   4        5     5268      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   2013-07   ...     2015-01  2015-02  2015-03  2015-04  2015-05  2015-06  \\\n",
       "0      0.0   ...         2.0      0.0      0.0      0.0      1.0      1.0   \n",
       "1      0.0   ...         0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2      0.0   ...         0.0      0.0      0.0      0.0      3.0      2.0   \n",
       "3      0.0   ...         0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "4      0.0   ...         0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   2015-07  2015-08  2015-09  2015-10  \n",
       "0      1.0      3.0      1.0      0.0  \n",
       "1      0.0      0.0      0.0      0.0  \n",
       "2      0.0      1.0      3.0      1.0  \n",
       "3      0.0      1.0      0.0      0.0  \n",
       "4      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##将数据进行组合\n",
    "test = pd.merge(test, data, on = ['item_id', 'shop_id'], how = 'left')\n",
    "##https://blog.csdn.net/weixin_37226516/article/details/64137043--merge的说明\n",
    "\n",
    "\n",
    "# 将空值填充为0\n",
    "test.fillna(0, inplace = True)\n",
    "\n",
    "# 看一下这个数据集\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建实际的训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train : (214200, 34)\n",
      "Shape of x_test : (214200, 34)\n",
      "Shape of y_train : (214200,)\n"
     ]
    }
   ],
   "source": [
    "# 现在让我们创建实际的训练数据\n",
    "\n",
    "x_train = test.drop(['2015-10', 'item_id', 'shop_id'], axis = 1)##和咱们提交格式有关\n",
    "y_train = test['2015-10']\n",
    "\n",
    "# 删除第一列，以便它可以预测未来的销售数据\n",
    "x_test = test.drop(['2013-01', 'item_id', 'shop_id'], axis = 1)#保持和训练集一样的特征数\n",
    "\n",
    "# 检查一下数据的维度看对不对\n",
    "print(\"Shape of x_train :\", x_train.shape)\n",
    "print(\"Shape of x_test :\", x_test.shape)\n",
    "print(\"Shape of y_train :\", y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据的拆分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  #将数据拆分为训练和有效数据集\n",
    "\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size = 0.2, random_state = 10)\n",
    "\n",
    "# # checking the shapes\n",
    "# print(\"Shape of x_train :\", x_train.shape)\n",
    "# print(\"Shape of x_valid :\", x_valid.shape)\n",
    "# print(\"Shape of y_train :\", y_train.shape)\n",
    "# print(\"Shape of y_valid :\", y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型的构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 23.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# 用GradientBoostingRegressor进行预测\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "GBR = GradientBoostingRegressor()\n",
    "GBR.fit(x_train, y_train)\n",
    "mode2 = GBR.predict(x_test).clip(0,20)##把预测之后的值限定在0到20之间\n",
    "joblib.dump(GBR, 'GBR2.pkl')##模型的保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode2=pd.DataFrame(mode2,columns=['item_cnt_month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>item_cnt_month</th>\n",
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      "text/plain": [
       "   item_cnt_month\n",
       "0        0.502150\n",
       "1        0.211889\n",
       "2        0.766678\n",
       "3        0.247797\n",
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     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mode2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode2.to_csv('submission.csv',index_label='ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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